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Aurora Vision Library

A comprehensive set of functions for creating industrial image analysis applications – from standard-based image acquisition interfaces, through low-level image processing routines, to ready-made tools such as template matching, measurements or barcode readers.

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Aurora Vision Library C++,. Net

Aurora Vision Library is a machine vision library for C++ and .NET programmers. It provides a comprehensive set of functions for creating industrial image analysis applications – from standard-based image acquisition interfaces, through low-level image processing routines, to ready-made tools such as template matching, measurements or barcode readers. The main strengths of the product include the highest performance, modern design and simple structure making it easy to integrate with the rest of your code.

The functions available in Aurora Vision Library closely correspond to the filters of Aurora Vision Studio. Therefore, it is possible to prototype your algorithms quickly in a graphical environment and then translate them to C++ or .NET, or even generate the C++ code automatically. Aurora Vision Library gives you instant access to highest quality, well optimized and field-tested code that you need for your machine vision projects!

Performance

Acceleration

In Aurora Vision Library careful design of algorithms goes hand in hand with extensive hardware optimizations, resulting in performance that puts the library among the fastest in the world. Our implementations make use of SSE instructions and parallel computations on multicore processors.

Modern Design

HMI

All types of data feature automatic memory management, errors are handled explicitly with exceptions and optional types are used for type-safe special values. All functions are thread-safe and use data parallelisminternally, when possible.

Simplicity & Consistency

Simplicity

The library is a simple collection of types and functions, provided as a single DLL file with appropriate headers. For maximum readability functions follow consistent naming convention (e.g. the VERB + NOUN form as in:SmoothImage, RotateVector). All results are returned via reference output parameters, so that many outputs are always possible.

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